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This code has the source code for the paper "Random Erasing Data Augmentation".
Thanks for Marcus D. Bloice, Marcus D. Bloice reproduces our method in Augmentor. Augmentor is an image augmentation library in Python for machine learning.
| Original image | Random Erasing |
|---|---|
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Requirements for Pytorch （see Pytorch installation instructions）
ResNet-20 baseline on CIFAR10：
python cifar.py --dataset cifar10 --arch resnet --depth 20
ResNet-20 + Random Erasing on CIFAR10：
python cifar.py --dataset cifar10 --arch resnet --depth 20 --p 0.5
ResNet-20 baseline on CIFAR100：
python cifar.py --dataset cifar100 --arch resnet --depth 20
ResNet-20 + Random Erasing on CIFAR100：
python cifar.py --dataset cifar100 --arch resnet --depth 20 --p 0.5
ResNet-20 baseline on Fashion-MNIST：
python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20
ResNet-20 + Random Erasing on Fashion-MNIST：
python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20 --p 0.5
For ResNet：
--arch resnet --depth (20， 32， 44， 56， 110)
For WRN：
--arch wrn --depth 28 --widen-factor 10
You can reproduce the results in our paper:
| CIFAR10 | CIFAR10 | CIFAR100 | CIFAR100 | Fashion-MNIST | Fashion-MNIST | |
|---|---|---|---|---|---|---|
| Models | Base. | +RE | Base. | +RE | Base. | +RE |
| ResNet-20 | 7.21 | 6.73 | 30.84 | 29.97 | 4.39 | 4.02 |
| ResNet-32 | 6.41 | 5.66 | 28.50 | 27.18 | 4.16 | 3.80 |
| ResNet-44 | 5.53 | 5.13 | 25.27 | 24.29 | 4.41 | 4.01 |
| ResNet-56 | 5.31 | 4.89 | 24.82 | 23.69 | 4.39 | 4.13 |
| ResNet-110 | 5.10 | 4.61 | 23.73 | 22.10 | 4.40 | 4.01 |
| WRN-28-10 | 3.80 | 3.08 | 18.49 | 17.73 | 4.01 | 3.65 |
If you have any questions about this code, please do not hesitate to contact us.